Tiny Object Detection in Aerial Images

Jinwang Wang, Wen Yang, Haowen Guo, Ruixiang Zhang, Gui-Song Xia

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Auto-TLDR; Tiny Object Detection in Aerial Images Using Multiple Center Points Based Learning Network

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Object detection in Earth Vision has achieved great progress in recent years. However, tiny object detection in aerial images remains a very challenging problem since the tiny objects contain a small number of pixels and are easily confused with the background. To advance tiny object detection research in aerial images, we present a new dataset for Tiny Object Detection in Aerial Images (AI-TOD). Specifically, AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others. To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset. Experimental results show that direct application of these approaches on AI-TOD produces suboptimal object detection results, thus new specialized detectors for tiny object detection need to be designed. Therefore, we propose a multiple center points based learning network (M-CenterNet) to improve the localization performance of tiny object detection, and experimental results show the significant performance gain over the competitors.

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Auto-TLDR; EAGLE: A Large-Scale Dataset for Multi-class Vehicle Detection with Object Orientation Information in Airborne Imagery

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Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

Dayang Yu, Rong Zhang, Shan Qin

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Auto-TLDR; Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

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SyNet: An Ensemble Network for Object Detection in UAV Images

Berat Mert Albaba, Sedat Ozer

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Auto-TLDR; SyNet: Combining Multi-Stage and Single-Stage Object Detection for Aerial Images

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Auto-TLDR; Multiple-Step Sampling for Dense Objects Detection

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MagnifierNet: Learning Efficient Small-Scale Pedestrian Detector towards Multiple Dense Regions

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Auto-TLDR; MagnifierNet: A Simple but Effective Small-Scale Pedestrian Detection Towards Multiple Dense Regions

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Auto-TLDR; Generative and Discriminative Learning for Small Object Detection

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Wei Xu, Yi Gan, Jianbo Su

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Auto-TLDR; BMFPN: Bidirectional Matrix Feature Pyramid Network for Object Detection

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Yi Gan, Wei Xu, Jianbo Su

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Auto-TLDR; SFPN: Semantic Feature Pyramid Network to Address Information Dilution Issue in FPN

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Construction Worker Hardhat-Wearing Detection Based on an Improved BiFPN

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Auto-TLDR; A One-Stage Object Detection Method for Hardhat-Wearing in Construction Site

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Work in the construction site is considered to be one of the occupations with the highest safety risk factor. Therefore, safety plays an important role in construction site. One of the most fundamental safety rules in construction site is to wear a hardhat. To strengthen the safety of the construction site, most of the current methods use multi-stage method for hardhat-wearing detection. These methods have limitations in terms of adaptability and generalizability. In this paper, we propose a one-stage object detection method based on convolutional neural network. We present a multi-scale strategy that selects the high-resolution feature maps of DarkNet-53 to effectively identify small-scale hardhats. In addition, we propose an improved weighted bi-directional feature pyramid network (BiFPN), which could fuse more semantic features from more scales. The proposed method can not only detect hardhat-wearing, but also identify the color of the hardhat. Experimental results show that the proposed method achieves a mAP of 87.04%, which outperforms several state-of-the-art methods on a public dataset.

Forground-Guided Vehicle Perception Framework

Kun Tian, Tong Zhou, Shiming Xiang, Chunhong Pan

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Auto-TLDR; A foreground segmentation branch for vehicle detection

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ScarfNet: Multi-Scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection

Jin Hyeok Yoo, Dongsuk Kum, Jun Won Choi

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Auto-TLDR; Semantic Fusion of Multi-scale Feature Maps for Object Detection

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CenterRepp: Predict Central Representative Point Set's Distribution for Detection

Yulin He, Limeng Zhang, Wei Chen, Xin Luo, Chen Li, Xiaogang Jia

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Auto-TLDR; CRPDet: CenterRepp Detector for Object Detection

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Small Object Detection Leveraging on Simultaneous Super-Resolution

Hong Ji, Zhi Gao, Xiaodong Liu, Tiancan Mei

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Auto-TLDR; Super-Resolution via Generative Adversarial Network for Small Object Detection

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Despite the impressive advancement achieved in object detection, the detection performance of small object is still far from satisfactory due to the lack of sufficient detailed appearance to distinguish it from similar objects. Inspired by the positive effects of super-resolution for object detection, we propose a general framework that can be incorporated with most available detector networks to significantly improve the performance of small object detection, in which the low-resolution image is super-resolved via generative adversarial network (GAN) in an unsupervised manner. In our method, the super-resolution network and the detection network are trained jointly and alternately with each other fixed. In particular, the detection loss is back-propagated into the super-resolution network during training to facilitate detection. Compared with available simultaneous super-resolution and detection methods which heavily rely on low-/high-resolution image pairs, our work breaks through such restriction via applying the CycleGAN strategy, achieving increased generality and applicability, while remaining an elegant structure. Extensive experiments on datasets from both computer vision and remote sensing communities demonstrate that our method works effectively on a wide range of complex scenarios, resulting in best performance that significantly outperforms many state-of-the-art approaches.

Hierarchical Head Design for Object Detectors

Shivang Agarwal, Frederic Jurie

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Auto-TLDR; Hierarchical Anchor for SSD Detector

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The notion of anchor plays a major role in modern detection algorithms such as the Faster-RCNN or the SSD detector. Anchors relate the features of the last layers of the detector with bounding boxes containing objects in images. Despite their importance, the literature on object detection has not paid real attention to them. The motivation of this paper comes from the observations that (i) each anchor learns to classify and regress candidate objects independently (ii) insufficient examples are available for each anchor in case of small-scale datasets. This paper addresses these questions by proposing a novel hierarchical head for the SSD detector. The new design has the added advantage of no extra weights, as compared to the original design at inference time, while improving detectors performance for small size training sets. Improved performance on PASCAL-VOC and state-of-the-art performance on FlickrLogos-47 validate the method. We also show when the proposed design does not give additional performance gain over the original design.

Detecting Objects with High Object Region Percentage

Fen Fang, Qianli Xu, Liyuan Li, Ying Gu, Joo-Hwee Lim

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Auto-TLDR; Faster R-CNN for High-ORP Object Detection

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Scene Text Detection with Selected Anchors

Anna Zhu, Hang Du, Shengwu Xiong

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Auto-TLDR; AS-RPN: Anchor Selection-based Region Proposal Network for Scene Text Detection

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Correlation-Based ConvNet for Small Object Detection in Videos

Brais Bosquet, Manuel Mucientes, Victor Brea

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Auto-TLDR; STDnet-ST: An End-to-End Spatio-Temporal Convolutional Neural Network for Small Object Detection in Video

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Triplet-Path Dilated Network for Detection and Segmentation of General Pathological Images

Jiaqi Luo, Zhicheng Zhao, Fei Su, Limei Guo

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Auto-TLDR; Triplet-path Network for One-Stage Object Detection and Segmentation in Pathological Images

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A Fast and Accurate Object Detector for Handwritten Digit String Recognition

Jun Guo, Wenjing Wei, Yifeng Ma, Cong Peng

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Auto-TLDR; ChipNet: An anchor-free object detector for handwritten digit string recognition

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Learning a Dynamic High-Resolution Network for Multi-Scale Pedestrian Detection

Mengyuan Ding, Shanshan Zhang, Jian Yang

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Auto-TLDR; Learningable Dynamic HRNet for Pedestrian Detection

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Pedestrian detection is a canonical instance of object detection in computer vision. In practice, scale variation is one of the key challenges, resulting in unbalanced performance across different scales. Recently, the High-Resolution Network (HRNet) has become popular because high-resolution feature representations are more friendly to small objects. However, when we apply HRNet for pedestrian detection, we observe that it improves for small pedestrians on one hand, but hurts the performance for larger ones on the other hand. To overcome this problem, we propose a learnable Dynamic HRNet (DHRNet) aiming to generate different network paths adaptive to different scales. Specifically, we construct a parallel multi-branch architecture and add a soft conditional gate module allowing for dynamic feature fusion. Both branches share all the same parameters except the soft gate module. Experimental results on CityPersons and Caltech benchmarks indicate that our proposed dynamic HRNet is more capable of dealing with pedestrians of various scales, and thus improves the performance across different scales consistently.

EDD-Net: An Efficient Defect Detection Network

Tianyu Guo, Linlin Zhang, Runwei Ding, Ge Yang

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Auto-TLDR; EfficientNet: Efficient Network for Mobile Phone Surface defect Detection

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As the most commonly used communication tool, the mobile phone has become an indispensable part of our daily life. The surface of the mobile phone as the main window of human-phone interaction directly affects the user experience. It is necessary to detect surface defects on the production line in order to ensure the high quality of the mobile phone. However, the existing mobile phone surface defect detection is mainly done manually, and currently there are few automatic defect detection methods to replace human eyes. How to quickly and accurately detect the surface defects of mobile phone is an urgent problem to be solved. Hence, an efficient defect detection network (EDD-Net) is proposed. Firstly, EfficientNet is used as the backbone network. Then, according to the small-scale of mobile phone surface defects, a feature pyramid module named GCSA-BiFPN is proposed to obtain more discriminative features. Finally, the box/class prediction network is used to achieve effective defect detection. We also build a mobile phone surface oil stain defect (MPSOSD) dataset to alleviate the lack of dataset in this field. The performance on the relevant datasets shows that the network we proposed is effective and has practical significance for industrial production.

Hybrid Cascade Point Search Network for High Precision Bar Chart Component Detection

Junyu Luo, Jinpeng Wang, Chin-Yew Lin

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Auto-TLDR; Object Detection of Chart Components in Chart Images Using Point-based and Region-Based Object Detection Framework

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Charts are commonly used for data visualization. One common form of chart distribution is in its image form. To enable machine comprehension of chart images, precise detection of chart components in chart images is a critical step. Existing image object detection methods do not perform well in chart component detection which requires high boundary detection precision. And traditional rule-based approaches lack enough generalization ability. In order to address this problem, we design a novel two-stage object detection framework that combines point-based and region-based ideas, by simulating the process that human creating bounding boxes for objects. The experiment on our labeled ChartDet dataset shows our method greatly improves the performance of chart object detection. We further extend our method to a general object detection task and get comparable performance.

ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection

Junting Fang, Xiaoyang Tan, Yuhui Wang

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Auto-TLDR; Attention Cascade R-CNN with Mix Non-Maximum Suppression for Robust Metal Defect Detection

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Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

Object Detection in the DCT Domain: Is Luminance the Solution?

Benjamin Deguerre, Clement Chatelain, Gilles Gasso

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Auto-TLDR; Jpeg Deep: Object Detection Using Compressed JPEG Images

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Object detection in images has reached unprecedented performances. The state-of-the-art methods rely on deep architectures that extract salient features and predict bounding boxes enclosing the objects of interest. These methods essentially run on RGB images. However, the RGB images are often compressed by the acquisition devices for storage purpose and transfer efficiency. Hence, their decompression is required for object detectors. To gain in efficiency, this paper proposes to take advantage of the compressed representation of images to carry out object detection usable in constrained resources conditions. Specifically, we focus on JPEG images and propose a thorough analysis of detection architectures newly designed in regard of the peculiarities of the JPEG norm. This leads to a x1.7 speed up in comparison with a standard RGB-based architecture, while only reducing the detection performance by 5.5%. Additionally, our empirical findings demonstrate that only part of the compressed JPEG information, namely the luminance component, may be required to match detection accuracy of the full input methods. Code is made available at : https://github.com/D3lt4lph4/jpeg_deep.

Efficient-Receptive Field Block with Group Spatial Attention Mechanism for Object Detection

Jiacheng Zhang, Zhicheng Zhao, Fei Su

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Auto-TLDR; E-RFB: Efficient-Receptive Field Block for Deep Neural Network for Object Detection

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Object detection has been paid rising attention in computer vision field. Convolutional Neural Networks (CNNs) extract high-level semantic features of images, which directly determine the performance of object detection. As a common solution, embedding integration modules into CNNs can enrich extracted features and thereby improve the performance. However, the instability and inconsistency of internal multiple branches exist in these modules. To address this problem, we propose a novel multibranch module called Efficient-Receptive Field Block (E-RFB), in which multiple levels of features are combined for network optimization. Specifically, by downsampling and increasing depth, the E-RFB provides sufficient RF. Second, in order to eliminate the inconsistency across different branches, a novel spatial attention mechanism, namely, Group Spatial Attention Module (GSAM) is proposed. The GSAM gradually narrows a feature map by channel grouping; thus it encodes the information between spatial and channel dimensions into the final attention heat map. Third, the proposed module can be easily joined in various CNNs to enhance feature representation as a plug-and-play component. With SSD-style detectors, our method halves the parameters of the original detection head and achieves high accuracy on the PASCAL VOC and MS COCO datasets. Moreover, the proposed method achieves superior performance compared with state-of-the-art methods based on similar framework.

FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings

Niels Ole Salscheider

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Auto-TLDR; FeatureNMS: Non-Maximum Suppression for Multiple Object Detection

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Most state of the art object detectors output multiple detections per object. The duplicates are removed in a post-processing step called Non-Maximum Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that contain objects with high overlap: The idea of this heuristic is that a high bounding box overlap corresponds to a high probability of having a duplicate. We propose FeatureNMS to solve this problem. FeatureNMS recognizes duplicates not only based on the intersection over union between bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance. Our approach outperforms classical NMS and derived approaches and achieves state of the art performance.

Deep Real-Time Hand Detection Using CFPN on Embedded Systems

Pirdiansyah Hendri, Jun-Wei Hsieh, Ping Yang Chen

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Auto-TLDR; Concatenated Feature Pyramid Network for Small Hand Detection on Embedded Devices

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Real-time HI (Human Interface) systems need accurate and efficient hand detection models to meet the limited resources in budget, dimension, memory, computing, and electric power. In recent years, object detection became a less challenging task with the latest deep CNN-based state-of-the-art models, i.e., RCNN, SSD, and YOLO; however, these models cannot provide the desired efficiency and accuracy for HI systems on embedded devices due to their complex time-consuming architecture. In addition, the detection of small hands (<30x30 pixels) is still a challenging task for all the above existing methods. Thus, we propose a shallow model named Concatenated Feature Pyramid Network (CFPN) to provide above mentioned performance for small hand detection. The superiority of CFPN is confirmed on a HandFlow dataset with mAP:0.5 of 95.6 and FPS of 33 on Nvidia TX2. The COCO dataset is also used to compare with other state-of-the-art method and shows the highest efficiency and accuracy with the proposed CFPN model. Thus we conclude that the proposed model is useful for real-life small hand detection on embedded devices.

Iterative Bounding Box Annotation for Object Detection

Bishwo Adhikari, Heikki Juhani Huttunen

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Auto-TLDR; Semi-Automatic Bounding Box Annotation for Object Detection in Digital Images

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Manual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming. In this paper, we propose a semi-automatic method for efficient bounding box annotation. The method trains the object detector iteratively on small batches of labeled images and learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors. We propose an experimental setup for simulating the human actions and use it for comparing different iteration strategies, such as the order in which the data is presented to the annotator. We experiment on our method with three datasets and show that it can reduce the human annotation effort significantly, saving up to 75% of total manual annotation work.

A Modified Single-Shot Multibox Detector for Beyond Real-Time Object Detection

Georgios Orfanidis, Konstantinos Ioannidis, Stefanos Vrochidis, Anastasios Tefas, Ioannis Kompatsiaris

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Auto-TLDR; Single Shot Detector in Resource-Restricted Systems with Lighter SSD Variations

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This works focuses on examining the performance of the Single Shot Detector (SSD) model in resource restricted systems where maintaining the power of the full model comprises a significant prerequisite. The proposed SSD variations examine the behavior of lighter versions of SSD while propose measures to limit the unavoidable performance shortage. The outcomes of the conducted research demonstrate a remarkable trade-off between performance losses, speed improvement and the required resource reservation. Thus, the experimental results evidence the efficiency of the presented SSD alterations towards accomplishing higher frame rates and retaining the performance of the original model.

Detective: An Attentive Recurrent Model for Sparse Object Detection

Amine Kechaou, Manuel Martinez, Monica Haurilet, Rainer Stiefelhagen

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Auto-TLDR; Detective: An attentive object detector that identifies objects in images in a sequential manner

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In this work, we present Detective – an attentive object detector that identifies objects in images in a sequential manner. Our network is based on an encoder-decoder architecture, where the encoder is a convolutional neural network, and the decoder is a convolutional recurrent neural network coupled with an attention mechanism. At each iteration, our decoder focuses on the relevant parts of the image using an attention mechanism, and then estimates the object’s class and the bounding box coordinates. Current object detection models generate dense predictions and rely on post-processing to remove duplicate predictions. Detective is a sparse object detector that generates a single bounding box per object instance. However, training a sparse object detector is challenging, as it requires the model to reason at the instance level and not just at the class and spatial levels. We propose a training mechanism based on the Hungarian Algorithm and a loss that balances the localization and classification tasks. This allows Detective to achieve promising results on the PASCAL VOC object detection dataset. Our experiments demonstrate that sparse object detection is possible and has a great potential for future developments in applications where the order of the objects to be predicted is of interest.

Yolo+FPN: 2D and 3D Fused Object Detection with an RGB-D Camera

Ya Wang

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Auto-TLDR; Yolo+FPN: Combining 2D and 3D Object Detection for Real-Time Object Detection

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In this paper we propose a new deep neural network system, called Yolo+FPN, which fuses both 2D and 3D object detection algorithms to achieve better real-time object detection results and faster inference speed, to be used on real robots. Finding an optimized fusion strategy to efficiently combine 3D object detection with 2D detection information is useful and challenging for both indoor and outdoor robots. In order to satisfy real-time requirements, a trade-off between accuracy and efficiency is needed. We not only have improved training and test accuracies and lower mean losses on the KITTI object detection benchmark, but also achieve better average precision on 3D detection of all classes in three levels of difficulty. Also, we implemented Yolo+FPN system using an RGB-D camera, and compared the speed of 2D and 3D object detection using different GPUs. For the real implementation of both indoor and outdoor scenes, we focus on person detection, which is the most challenging and important among the three classes.

DualBox: Generating BBox Pair with Strong Correspondence Via Occlusion Pattern Clustering and Proposal Refinement

Zheng Ge, Chuyu Hu, Xin Huang, Baiqiao Qiu, Osamu Yoshie

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Auto-TLDR; R2NMS: Combining Full and Visible Body Bounding Box for Dense Pedestrian Detection

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Despite the rapid development of pedestrian detection, the problem of dense pedestrian detection is still unsolved, especially the upper limit of Recall caused by Non-Maximum-Suppression (NMS). Out of this reason, R2NMS is proposed to simultaneously detect full and visible body bounding boxes, by replacing the full body BBoxes with less occluded visible body BBoxes in the NMS algorithm, achieving a higher recall. However, the P-RPN and P-RCNN modules proposed in R2NMS for simultaneous high quality full and visible body prediction require non-trivial positive/negative assigning strategies for anchor BBoxes. To simplify the prerequisites and improve the utility of R2NMS, we incorporate clustering analysis into the learning of visible body proposals from full body proposals. Furthermore, to reduce the computation complexity caused by the large number of potential visible body proposals, we introduce a novel occlusion pattern prediction branch on top of the R-CNN module (i.e. F-RCNN) to select the best matched visible proposals for each full body proposals and then feed them into another R-CNN module (i.e. V-RCNN). Incorporated with R2NMS, our DualBox model can achieve competitive performance while only requires few hyper-parameters. We validate the effectiveness of the proposed approach on the CrowdHuman and CityPersons datasets. Experimental results show that our approach achieves promising performance for detecting both non-occluded and occluded pedestrians, especially heavily occluded ones.

A Novel Region of Interest Extraction Layer for Instance Segmentation

Leonardo Rossi, Akbar Karimi, Andrea Prati

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Auto-TLDR; Generic RoI Extractor for Two-Stage Neural Network for Instance Segmentation

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Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extract a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome to the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP on bounding box detection and 1.7% AP on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection-groie

HPERL: 3D Human Pose Estimastion from RGB and LiDAR

Michael Fürst, Shriya T.P. Gupta, René Schuster, Oliver Wasenmüler, Didier Stricker

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Auto-TLDR; 3D Human Pose Estimation Using RGB and LiDAR Using Weakly-Supervised Approach

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In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving. The current state-of-the-art is focused only on RGB and RGB-D approaches for predicting the 3D human pose. However, not using precise LiDAR depth information limits the performance and leads to very inaccurate absolute pose estimation. With LiDAR sensors becoming more affordable and common on robots and autonomous vehicle setups, we propose an end-to-end architecture using RGB and LiDAR to predict the absolute 3D human pose with unprecedented precision. Additionally, we introduce a weakly-supervised approach to generate 3D predictions using 2D pose annotations from PedX. This allows for many new opportunities in the field of 3D human pose estimation.

The DeepScoresV2 Dataset and Benchmark for Music Object Detection

Lukas Tuggener, Yvan Putra Satyawan, Alexander Pacha, Jürgen Schmidhuber, Thilo Stadelmann

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Auto-TLDR; DeepScoresV2: an extended version of the DeepScores dataset for optical music recognition

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In this paper, we present DeepScoresV2, an extended version of the DeepScores dataset for optical music recognition (OMR). We improve upon the original DeepScores dataset by providing much more detailed annotations, namely (a) annotations for 135 classes including fundamental symbols of non-fixed size and shape, increasing the number of annotated symbols by 23%; (b) oriented bounding boxes; (c) higher-level rhythm and pitch information (onset beat for all symbols and line position for noteheads); and (d) a compatibility mode for easy use in conjunction with the MUSCIMA++ dataset for OMR on handwritten documents. These additions open up the potential for future advancement in OMR research. Additionally, we release two state-of-the-art baselines for DeepScoresV2 based on Faster R-CNN and the Deep Watershed Detector. An analysis of the baselines shows that regular orthogonal bounding boxes are unsuitable for objects which are long, small, and potentially rotated, such as ties and beams, which demonstrates the need for detection algorithms that naturally incorporate object angles. Dataset, code and pre-trained models, as well as user instructions, are publicly available at https://tuggeluk.github.io/dsv2_preview/

CASNet: Common Attribute Support Network for Image Instance and Panoptic Segmentation

Xiaolong Liu, Yuqing Hou, Anbang Yao, Yurong Chen, Keqiang Li

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Auto-TLDR; Common Attribute Support Network for instance segmentation and panoptic segmentation

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Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentation, instance segmentation can provide more analytical results at pixel level. Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes. CASNet is designed in the manner of fully convolutional and can implement training and inference from end to end. And CASNet manages predicting the instance without overlaps and holes, which problem exists in most of current instance segmentation algorithms. Furthermore, it can be easily extended to panoptic segmentation through minor modifications with little computation overhead. CASNet builds a bridge between semantic and instance segmentation from finding pixel class ID to obtaining class and instance ID by operations on common attribute. Through experiment for instance and panoptic segmentation, CASNet gets mAP 32.8\% and PQ 59.0\% on Cityscapes validation dataset by joint training, and mAP 36.3\% and PQ 66.1\% by separated training mode. For panoptic segmentation, CASNet gets state-of-the-art performance on the Cityscapes validation dataset.

Multi-View Object Detection Using Epipolar Constraints within Cluttered X-Ray Security Imagery

Brian Kostadinov Shalon Isaac-Medina, Chris G. Willcocks, Toby Breckon

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Auto-TLDR; Exploiting Epipolar Constraints for Multi-View Object Detection in X-ray Security Images

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Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object detection labels is proposed. In addition, detections are given a score based on its similarity with respect to the distribution of the error of the epipolar estimation. This score is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.

PRF-Ped: Multi-Scale Pedestrian Detector with Prior-Based Receptive Field

Yuzhi Tan, Hongxun Yao, Haoran Li, Xiusheng Lu, Haozhe Xie

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Auto-TLDR; Bidirectional Feature Enhancement Module for Multi-Scale Pedestrian Detection

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Multi-scale feature representation is a common strategy to handle the scale variation in pedestrian detection. Existing methods simply utilize the convolutional pyramidal features for multi-scale representation. However, they rarely pay attention to the differences among different feature scales and extract multi-scale features from a single feature map, which may make the detectors sensitive to scale-variance in multi-scale pedestrian detection. In this paper, we introduce a bidirectional feature enhancement module (BFEM) to augment the semantic information of low-level features and the localization information of high-level features. In addition, we propose a prior-based receptive field block (PRFB) for multi-scale pedestrian feature extraction, where the receptive field is closer to the aspect ratio of the pedestrian target. Consequently, it is less affected by the surrounding background when extracting features. Experimental results indicate that the proposed method outperform the state-of-the-art methods on the CityPersons and Caltech datasets.

End-To-End Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

Yongsheng Bai, Alper Yilmaz, Halil Sezen

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Auto-TLDR; Robust Mask R-CNN for Crack Detection in Extreme Events

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Robust Mask R-CNN (Mask Regional Convolutional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High-resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.

RLST: A Reinforcement Learning Approach to Scene Text Detection Refinement

Xuan Peng, Zheng Huang, Kai Chen, Jie Guo, Weidong Qiu

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Auto-TLDR; Saccadic Eye Movements and Peripheral Vision for Scene Text Detection using Reinforcement Learning

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Within the research of scene text detection, some previous work has already achieved significant accuracy and efficiency. However, most of the work was generally done without considering about the implicit relationship between detection and eye movements. In this paper, we propose a new method for scene text detection especially for its refinement based on reinforcement learning. The idea of this method is inspired by Saccadic Eye Movements and Peripheral Vision. A saccade makes it possible for humans to orient the gaze to the location where a visual object has appeared. Peripheral vision gathers visual information of surroundings which provides supplement to foveal vision during gazing. We propose a simple pipeline, imitating the way human eyes do a saccade and collect peripheral information, to locate scene text roughly and to refine multi-scale vision field iteratively using reinforcement learning. For both training and evaluation, we use ICDAR2015 Challenge 4 dataset as a base and design several criteria to measure the feasibility of our work.

Temporal Feature Enhancement Network with External Memory for Object Detection in Surveillance Video

Masato Fujitake, Akihiro Sugimoto

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Auto-TLDR; Temporal Attention Based External Memory Network for Surveillance Object Detection

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Video object detection is challenging and essential in practical applications, such as surveillance cameras for traffic control and public security. Unlike the video in natural scenes, the surveillance video tends to contain dense, and small objects (typically vehicles) in their appearances. Therefore, existing methods for surveillance object detection utilize still-image object detection approaches with rich feature extractors at the expense of their run-time speeds. The run-time speed, however, becomes essential when the video is being streamed. In this paper, we exploit temporal information in videos to enrich the feature maps, proposing the first temporal attention based external memory network for the live stream of video. Extensive experiments on real-world traffic surveillance benchmarks demonstrate the real-time performance of the proposed model while keeping comparable accuracy with state-of-the-art.

Point In: Counting Trees with Weakly Supervised Segmentation Network

Pinmo Tong, Shuhui Bu, Pengcheng Han

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Auto-TLDR; Weakly Tree counting using Deep Segmentation Network with Localization and Mask Prediction

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For tree counting tasks, since traditional image processing methods require expensive feature engineering and are not end-to-end frameworks, this will cause additional noise and cannot be optimized overall, so this method has not been widely used in recent trends of tree counting application. Recently, many deep learning based approaches are designed for this task because of the powerful feature extracting ability. The representative way is bounding box based supervised method, but time-consuming annotations are indispensable for them. Moreover, these methods are difficult to overcome the occlusion or overlap. To solve this problem, we propose a weakly tree counting network (WTCNet) based on deep segmentation network with only point supervision. It can simultaneously complete tree counting with localization and output mask of each tree at the same time. We first adopt a novel feature extractor network (FENet) to get features of input images, and then an effective strategy is introduced to deal with different mask predictions. In the end, we propose a basic localization guidance accompany with rectification guidance to train the network. We create two different datasets and select an existing challenging plant dataset to evaluate our method on three different tasks. Experimental results show the good performance improvement of our method compared with other existing methods. Further study shows that our method has great potential to reduce human labor and provide effective ground-truth masks and the results show the superiority of our method over the advanced methods.

One-Stage Multi-Task Detector for 3D Cardiac MR Imaging

Weizeng Lu, Xi Jia, Wei Chen, Nicolò Savioli, Antonio De Marvao, Linlin Shen, Declan O'Regan, Jinming Duan

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Auto-TLDR; Multi-task Learning for Real-Time, simultaneous landmark location and bounding box detection in 3D space

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Fast and accurate landmark location and bounding box detection are important steps in 3D medical imaging. In this paper, we propose a novel multi-task learning framework, for real-time, simultaneous landmark location and bounding box detection in 3D space. Our method extends the famous single-shot multibox detector (SSD) from single-task learning to multi-task learning and from 2D to 3D. Furthermore, we propose a post-processing approach to refine the network landmark output, by averaging the candidate landmarks. Owing to these settings, the proposed framework is fast and accurate. For 3D cardiac magnetic resonance (MR) images with size 224 × 224 × 64, our framework runs about 128 volumes per second (VPS) on GPU and achieves 6.75mm average point-to-point distance error for landmark location, which outperforms both state-of-the-art and baseline methods. We also show that segmenting the 3D image cropped with the bounding box results in both improved performance and efficiency.

Nighttime Pedestrian Detection Based on Feature Attention and Transformation

Gang Li, Shanshan Zhang, Jian Yang

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Auto-TLDR; FAM and FTM: Enhanced Feature Attention Module and Feature Transformation Module for nighttime pedestrian detection

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Pedestrian detection at nighttime is an important yet challenging task, which is fundamental for many practical applications, e.g. autonomous driving, video surveillance. To address this problem, in this work we start with some analysis, from which we find that the nighttime features have much more noise than that of daytime, resulting in low discrimination ability. Besides, we also observe some pedestrian examples are under adverse illumination conditions, and they can hardly provide sufficient information for accurate detection. Based on these findings, we propose the Feature Attention Module (FAM) and Feature Transformation Module (FTM) to enhance nighttime features. In FAM, guided by progressive segmentation supervision, hierarchical feature attention is produced to enhance multi-level features. On the other hand, FTM is introduced to enforce features from adverse illumination to approach that from better illumination. Based on feature attention and transformation (FAT) mechanism, a two-stage detector called FATNet is constructed for nighttime pedestrian detection. We conduct extensive experiments on nighttime datasets of EuroCity Persons (Night) and NightOwls to demonstrate the effectiveness of our method. On both two datasets, our method achieves significant improvements to the baseline and also outperforms state-of-the-art detectors.

PointDrop: Improving Object Detection from Sparse Point Clouds Via Adversarial Data Augmentation

Wenxin Ma, Jian Chen, Qing Du, Wei Jia

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Auto-TLDR; PointDrop: Improving Robust 3D Object Detection to Sparse Point Clouds

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Current 3D object detection methods achieve accurate and efficient results on the standard point cloud dataset. However, in real-world applications, due to the expensive cost of obtaining the annotated 3D object detection data, we expect to directly apply the model trained on the standard dataset to real-world scenarios. This strategy may fail because the point cloud samples obtained in the real-world scenarios may be much sparser due to various reasons (occlusion, low reflectivity of objects and fewer laser beams) and existing methods do not consider the limitations of their models on sparse point clouds. To improve the robustness of an object detector to sparser point clouds, we propose PointDrop, which learns to drop the features of some key points in the point clouds to generate challenging sparse samples for data augmentation. Moreover, PointDrop is able to adjust the difficulty of the generated samples based on the capacity of the detector and thus progressively improve the performance of the detector. We create two sparse point clouds datasets from the KITTI dataset to evaluate our method, and the experimental results show that PointDrop significantly improves the robustness of the detector to sparse point clouds.

StrongPose: Bottom-up and Strong Keypoint Heat Map Based Pose Estimation

Niaz Ahmad, Jongwon Yoon

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Auto-TLDR; StrongPose: A bottom-up box-free approach for human pose estimation and action recognition

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Adaptation of deep convolutional neural network has made revolutionary progress in human pose estimation, various applications in recent years have drawn considerable attention. However, prediction and localization of the keypoints in single and multi-person images are a challenging problem. Towards this purpose, we present a bottom-up box-free approach for the task of pose estimation and action recognition. We proposed a StrongPose system model that uses part-based modeling to tackle object-part associations. The model utilizes a convolution network that learns how to detect Strong Keypoints Heat Maps (SKHM) and predict their comparative displacements, enabling us to group keypoints into person pose instances. Further, we produce Body Heat Maps (BHM) with the help of keypoints which allows us to localize the human body in the picture. The StrongPose framework is based on fully-convolutional engineering and permits proficient inference, with runtime basically autonomous of the number of individuals display within the scene. Train and test on COCO data alone, our framework achieves COCO test-dev keypoint average precision of 0.708 using ResNet-101 and 0.725 using ResNet-152, which considerably outperforms all prior bottom-up pose estimation frameworks.

CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images

Madhav Agarwal, Ajoy Mondal, C. V. Jawahar

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Auto-TLDR; CDeC-Net: An End-to-End Trainable Deep Network for Detecting Tables in Document Images

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Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (CDeC-Net) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. We empirically evaluate CDeC-Net on all the publicly available benchmark datasets— ICDAR-2013, ICDAR-2017, ICDAR-2019, UNLV, Marmot, PubLayNet, TableBank, and IIIT-AR-13K —with extensive experiments. Our solution has three important properties:(i) a single trained model CDeC-Net‡ performs well across all the popular benchmark datasets; (ii) we report excellent performances across multiple, including higher, thresholds of IoU; (iii) by following the same protocol of the recent papers for each of the benchmarks, we consistently demonstrate the superior quantitative performance. Our code and models will be publicly released for enabling reproducibility of the results.

Efficient Grouping for Keypoint Detection

Alexey Sidnev, Ekaterina Krasikova, Maxim Kazakov

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Auto-TLDR; Automatic Keypoint Grouping for DeepFashion2 Dataset

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DeepFashion2 dataset raises a new challenge for a keypoint detection task. It contains 13 categories with a different number of keypoints, 294 in total. Direct prediction of all keypoints leads to huge memory consumption, slow training, and inference speed. This paper presents a study of keypoint grouping approach and how it affects performance on the example of CenterNet architecture. We propose a simple and efficient automatic grouping technique and apply it to DeepFashion2 fashion landmark task and MS COCO Human Pose task. It allows reducing memory consumption up to 30%, decreasing inference time up to 30%, and training time up to 26% without compromising accuracy.